Intelligent VR teaching system method, system and device for enhancing video teaching effect

Abstract

The invention relates to an intelligent VR teaching method for enhancing a video teaching effect. The method comprises the following steps: selecting a target teaching video, and playing a theme sub-video in the target teaching video; acquiring expression data of a user within the playing time range of the theme sub-video; identifying expression change features, calculating a user understanding degree in a theme self-adaptive playing time range, and calculating a user understanding degree in a granulated sub-video time range; judging whether the teaching content of the theme sub-video needs to be adjusted or not; selecting alternative granulated sub-videos from a teaching video database; combining the selected alternative granulated sub-videos into an updated theme sub-video, and playing the updated theme sub-video; and when the remaining learning time is less than the updated time length of the theme sub-video, stopping playing the teaching video. According to the method, the teaching method is adaptively adjusted according to user course understanding degree data fed back by video teaching, and the video teaching effect is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet data analysis, and in particular to an intelligent VR teaching system method, system and device for enhancing video teaching effect. Background Art

[0002] Virtual simulation technology is applied to video teaching, which can make teaching more interesting and improve students' enthusiasm for learning. In order to provide feedback on teaching quality, methods such as electroencephalogram, eye tracking, expression and emotion analysis are used to analyze and evaluate teaching effects; at the same time, according to the teaching effects, teachers can adjust teaching content and teaching methods to ensure students' classroom experience. The adjustment of teaching content and teaching methods in existing technologies requires teacher participation and cannot enhance the teaching effect of video teaching.

[0003] For example, patent publication number CN116778543A discloses a classroom teaching effect detection system, which uses a human posture device and a facial recognition device to sense and identify students' bad classroom behaviors, and compares them with thresholds to determine students' bad classroom behaviors. When there are more violations, teachers will be reminded to further control the classroom order and atmosphere. This method is a classroom discipline detection method, which requires teachers to adjust teaching content and methods according to the detection results, and this detection method cannot detect students' understanding of the teaching content.

[0004] Patent publication number CN116453191A discloses a classroom teaching quality assessment method, device, equipment and readable storage medium, which inputs the human behavior image information of each student in the learning process into a pre-trained behavior recognition model, and outputs the behavior results of each student in the learning process; inputs the facial image information of each student in the learning process into a pre-trained expression recognition model, and outputs the expression results of each student in the learning process; calculates the comprehensive evaluation value according to the proportion of the number of negative behaviors in the expression results in the total amount of all behavior results and the proportion of the number of negative expressions in the total amount of all expression results; compares the comprehensive evaluation value with the preset teaching quality threshold to obtain the teaching quality assessment result. It reflects the students' understanding level through the students' expressions to a certain extent, but this method cannot realize adaptive adjustment of teaching methods according to the user's course understanding level data fed back by video teaching.

[0005] Video teaching with autonomous enhancement capabilities can improve students' understanding and enhance students' autonomous learning effects. Therefore, it is particularly important to intelligently and autonomously adjust teaching content and teaching methods according to students' understanding of VR video teaching, so as to achieve the purpose of enhancing the teaching effect of VR video teaching. Summary of the invention

[0006] The purpose of the present invention is to solve the problems existing in the above-mentioned prior art. The present invention provides an intelligent VR teaching method and system for enhancing the video teaching effect. The method focuses on a granular knowledge point, a page of PPT and other teaching contents, and adaptively adjusts the teaching method according to the user's course understanding degree data fed back by the video teaching to enhance the video teaching effect.

[0007] The present invention is achieved through the following technical solutions:

[0008] An intelligent VR teaching method for enhancing video teaching effect comprises the following steps:

[0009] S1. The user selects a target teaching video and sets the maximum learning time acceptable to the user, and then plays a theme sub-video in the target teaching video, wherein the theme sub-video includes a plurality of granular sub-videos;

[0010] S2, collecting the user's facial expression data within the time range of the theme sub-video playback;

[0011] S3, identifying the expression change characteristics of the user expression data collected in step S2, calculating the user's understanding degree within the self-adaptive playback time range of the theme sub-video, and calculating the user's understanding degree within the granular sub-video time range;

[0012] S4, judging whether it is necessary to adjust the teaching content of the theme sub-video according to the user's understanding degree within the playing time range of the theme sub-video, if necessary, adjust to the next theme sub-video, and then execute step S2; if not, proceed to step S5;

[0013] S5, judging whether it is necessary to select an alternative granular sub-video from the teaching video database according to the user's understanding degree within the time range of the granular sub-video, and if so, proceeding to step S6;

[0014] S6. Combining the selected candidate granular sub-videos into an updated theme sub-video, and playing the updated theme sub-video, and then executing step S2; when the remaining learning time is less than the time length of the updated theme sub-video, stop playing the teaching video.

[0015] Furthermore, the specific method for determining whether the teaching content of the theme sub-video needs to be adjusted in step S4 includes:

[0016] If the user's understanding level within the time range of the theme sub-video playback is ≥ the first given threshold, there is no need to adjust the teaching content in the theme sub-video, and the next theme sub-video is played directly, and then step S2 is executed, and when the remaining learning time is less than the time length of the next theme sub-video, the teaching video is stopped;

[0017] If the user's understanding level within the theme sub-video playback time range is less than the first given threshold, it is necessary to adjust the teaching content of the current theme sub-video according to the user's understanding level within the granular sub-video time range, and then execute step S5.

[0018] Furthermore, the step S5 specifically includes:

[0019] If the user's understanding level of a granular sub-video in the subject sub-video is less than a second given threshold, randomly selecting an alternative granular sub-video from the alternative granular sub-videos of the granular sub-video;

[0020] If the user's understanding level of a granular sub-video in the theme sub-video is ≥ a second given threshold, the granular sub-video and its alternative granular sub-videos will no longer be played.

[0021] A second aspect of the present invention provides an intelligent VR teaching system for enhancing video teaching effects, the system comprising a VR helmet interaction subsystem and a teaching management subsystem;

[0022] The VR helmet interaction subsystem includes

[0023] An interactive interface unit, configured to provide a teaching video, an acceptable maximum learning time dialog box, play the teaching video, and set the acceptable maximum learning time;

[0024] An expression data collection unit is configured to use a camera to collect expression data of a user at a given frequency within a time range of playing the theme sub-video; the expression data includes an eye image and a mouth image; and

[0025] A communication unit, configured to perform data interaction with the teaching management subsystem;

[0026] The teaching management subsystem includes

[0027] An expression recognition unit configured to recognize the changing features of the user's expression within the playing time range of the theme sub-video;

[0028] An understanding degree calculation unit configured to calculate the user's understanding degree within the time range of the theme sub-video playback and to calculate the user's understanding degree within the time range of the granular sub-video;

[0029] An adjustment judgment unit is configured to judge whether the teaching content of the theme sub-video needs to be adjusted according to the user's understanding level within the playing time range of the theme sub-video;

[0030] an alternative video generating unit configured to select alternative granular sub-videos from a teaching video database according to a user's understanding degree within a time range of the granular sub-videos, and combine the selected alternative granular sub-videos into an updated theme sub-video;

[0031] A teaching video database configured to store a plurality of teaching videos and candidate granular sub-videos of the granular sub-videos in each teaching video; and

[0032] The communication unit is configured to send and receive request information for playing teaching videos, configuration information such as the maximum acceptable learning time, expression data, and teaching videos; send and receive instructions for playing updated theme sub-videos and instructions for playing the next theme sub-video.

[0033] The third aspect of the present invention also provides a device of an intelligent VR teaching system for enhancing the video teaching effect.

[0034] Compared with the prior art, the beneficial effects of the present invention include:

[0035] The intelligent VR teaching method for enhancing the video teaching effect of the present invention focuses on short-term teaching content such as a granular knowledge point, a page of PPT, etc., and adaptively adjusts the teaching method according to the user's course comprehension data fed back by the video teaching to enhance the video teaching effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flowchart of an embodiment of an intelligent VR teaching method for enhancing video teaching effect according to the present invention;

[0037] Figure 2 A schematic diagram of a method for adjusting a theme sub-video and a granular sub-video according to the present invention;

[0038] Figure 3 This is a structural diagram of a specific implementation of the intelligent VR teaching system for enhancing video teaching effects of the present invention. DETAILED DESCRIPTION

[0039] The intelligent VR teaching method of the present invention for enhancing the video teaching effect is to divide a teaching video into several theme sub-videos around a theme, and each theme sub-video is divided into granular sub-videos with shorter time length; according to the user's course understanding degree data fed back by the video teaching, the teaching method is adaptively adjusted to enhance the video teaching effect.

[0040] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0041] refer to Figure 1 As shown, the intelligent VR teaching method for enhancing the video teaching effect of the present invention specifically includes the following steps:

[0042] S1. The user selects a target teaching video and sets the maximum learning time acceptable to the user, and then plays a theme sub-video in the target teaching video, wherein the theme sub-video includes a plurality of granular sub-videos;

[0043] S2, collecting the user's facial expression data within the time range of the theme sub-video playback;

[0044] S3, identifying the expression change characteristics of the user expression data collected in step S2, calculating the user's understanding degree within the self-adaptive playback time range of the theme sub-video, and calculating the user's understanding degree within the granular sub-video time range;

[0045] S4, judging whether it is necessary to adjust the teaching content of the theme sub-video according to the user's understanding degree within the playing time range of the theme sub-video, if necessary, adjust to the next theme sub-video, and then execute step S2; if not, proceed to step S5;

[0046] S5, judging whether it is necessary to select an alternative granular sub-video from the teaching video database according to the user's understanding degree within the time range of the granular sub-video, and if so, proceeding to step S6;

[0047] S6. Combining the selected candidate granular sub-videos into an updated theme sub-video, and playing the updated theme sub-video, and then executing step S2; when the remaining learning time is less than the time length of the updated theme sub-video, stop playing the teaching video.

[0048] The technical solution of the present invention adaptively adjusts the teaching plan according to the degree of understanding of knowledge points by different students, ensuring that students with poor learning ability can fully understand the knowledge, and students with good learning ability can learn knowledge points more efficiently. Through this adaptively adjusted teaching method, personalized teaching methods are provided for different students, greatly improving the video teaching effect.

[0049] The intelligent VR teaching method for enhancing the video teaching effect of the present invention comprises the following steps in a specific implementation scheme:

[0050] S1. The user selects a teaching video, sets the longest acceptable learning time, and plays the theme sub-videos in the teaching video in sequence; a teaching video can be divided into several theme sub-videos around a theme, and each theme sub-video is divided into granular sub-videos with shorter time lengths, and the granular sub-videos further pre-store corresponding candidate granular sub-videos according to the relevance and difficulty of the granular knowledge points. When the present invention is used, the content of the current theme sub-video is composed of granular sub-videos, and the content of the current theme sub-video can be reorganized and updated by selecting a more suitable granular sub-video according to the user's understanding level.

[0051] In a more specific method, a timed aiming point algorithm is used to select the teaching video to be watched and set the longest acceptable learning time. After the user wears the VR helmet, an aiming point is configured in front of the user's eyes. The aiming point moves synchronously with the user's head. The user sequentially places the aiming point at the position of the teaching video to be selected and the longest acceptable learning time to be selected. After 2 to 3 seconds, a click operation will be triggered, thereby completing the selection operation of the teaching video and the selection operation of the longest acceptable learning time.

[0052] S2. Collect the user's expression data within the time range of the theme sub-video playback; collect the user's expression data at a given frequency within the time range of the theme sub-video playback. The expression data includes eye images and mouth images. In order to better collect the students' expression data, the system is configured with two cameras, one camera collects the user's eye image, and the other camera collects the user's mouth image. The camera can be set on the VR helmet or separately.

[0053] S3. Identify the features of the user's facial expression changes within the time range of the theme sub-video playback, calculate the user's understanding level within the time range of the theme sub-video playback, and calculate the user's understanding level within the time range of the granular sub-video of the current theme sub-video.

[0054] Convolutional neural networks are used to identify the characteristics of user expression changes; the characteristics of user expression changes include blinking frequency, frowning frequency, and pursing mouth frequency.

[0055] According to the recognition results of the user's facial expression changes, the user's course understanding level is calculated.

[0056] Specifically, the formula for calculating the user's course understanding level is:

[0057] S=a×(A-A0)+b×(B-B0)+c×(C-C0)

[0058] Among them, a, b and c represent the weight of blinking, frowning and pursing lips respectively, A0, B0 and C0 represent the preset frequency of blinking, frowning and pursing lips respectively, and A, B and C represent the frequency of blinking, frowning and pursing lips output by recognition respectively. From the calculation formula of the degree of understanding, it can be seen that the larger the S value, the lower the user's degree of understanding; conversely, the smaller the S value, the higher the user's degree of understanding.

[0059] S4. Determine whether the teaching content of the theme sub-video needs to be adjusted based on the user's understanding level within the playing time range of the theme sub-video.

[0060] like Figure 2As shown, if the user's understanding level within the theme sub-video playback time range is ≥ the first given threshold, that is, the current theme sub-video content is not suitable for the user's understanding level, there is no need to adjust the teaching content of the current theme sub-video, and the next theme sub-video can be played directly. After that, step S2 is executed; when the remaining learning time is less than the time length of the next theme sub-video, stop playing the teaching video.

[0061] If the user's understanding level within the theme sub-video playback time range is less than the first given threshold, and the current theme sub-video content is adapted to the user's understanding level, then it is further determined whether the teaching content of the theme sub-video needs to be adjusted based on the user's understanding level within the granular sub-video time range, and step S5 is specifically executed.

[0062] S5. Select an alternative granular sub-video in the corresponding theme sub-video from the teaching video database according to the user's understanding level within the time range of the granular sub-video.

[0063] If the user's understanding level of a granular sub-video in the theme sub-video is less than the second given threshold, that is, the user can understand the current content well, he / she continues to learn the current granular sub-video; at the same time, an alternative granular sub-video is randomly selected from the alternative granular sub-videos of the granular sub-video.

[0064] If the user's understanding level of a granular sub-video in the theme sub-video is ≥ the second given threshold, that is, the user cannot understand the current content, the granular sub-video and its alternative granular sub-video will no longer be played.

[0065] S6. Combine the candidate granular sub-videos selected in step S5 into an updated theme sub-video, and play the updated theme sub-video. Then, execute step S2. When the remaining learning time is less than the time length of the updated theme sub-video, stop playing the teaching video.

[0066] In this method, the subject sub-videos are continuously updated and reorganized according to the user's understanding level in the learning state, so as to construct subject sub-videos that are most suitable for the user's course understanding ability, thereby enhancing the video teaching effect.

[0067] In another example, step S2 in the intelligent VR teaching method for enhancing video teaching effect also includes safety monitoring of the user's physiological health, specifically including passive infrared thermometer monitoring and eye image detection.

[0068] Basic data is collected through a passive human infrared temperature measurement system. The infrared energy emitted by the human body is collected and captured by sensors to generate a stable infrared image, thereby distinguishing whether the user has a fever.

[0069] The camera is used to collect images of the target organ (specifically the eye), and the shooting parameter rules are intelligently set. The shooting brightness and shooting parameters are automatically modified through environmental recognition. The YCbCr technology is used to calculate the chromaticity, separate the color and brightness of the image, and analyze and calculate to obtain the two-dimensional image distribution results. An eye color analysis model is established. The analysis and calculation method is as follows: set the normal color and range of the eye, and the color and range of the eye in a pathological state; determine whether there is a physiological disease by color.

[0070] When no physiological disease is detected, step S3 is continued to be performed normally.

[0071] When it is detected that the user has a physiological disease, the user is reminded of the possibility of the disease and is given the option of whether to continue the intelligent teaching program; if so, continue to step S3 and replace the first given threshold and the second given threshold within the video playback time range with the first given threshold and the second given threshold in the disease case.

[0072] Among them, the first given threshold value under the disease condition is greater than the first given threshold value under the normal condition; the second given threshold value under the disease condition is greater than the second given threshold value under the normal condition.

[0073] The second aspect of the present invention also discloses an intelligent VR teaching system for enhancing the video teaching effect. Figure 3 As shown, the system includes a VR helmet interaction subsystem and a teaching management subsystem;

[0074] The VR helmet interaction subsystem includes

[0075] An interactive interface unit, configured to provide a teaching video, an acceptable maximum learning time dialog box, play the teaching video, and set the acceptable maximum learning time;

[0076] An expression data collection unit is configured to collect the expression data of the user using a camera at a given frequency within the playing time range of the theme sub-video; the expression data includes an eye image and a mouth image; in a specific example, the unit is configured with two cameras, one camera is used to collect the eye image of the user, and the other camera is used to collect the mouth image of the user; and

[0077] A communication unit, configured to perform data interaction with the teaching management subsystem;

[0078] The teaching management subsystem includes

[0079] An expression recognition unit configured to recognize the changing features of the user's expression within the playing time range of the theme sub-video;

[0080] An understanding degree calculation unit configured to calculate the user's understanding degree within the time range of the theme sub-video playback and to calculate the user's understanding degree within the time range of the granular sub-video;

[0081] An adjustment judgment unit is configured to judge whether the teaching content of the theme sub-video needs to be adjusted according to the user's understanding level within the playing time range of the theme sub-video;

[0082] an alternative video generating unit configured to select alternative granular sub-videos from a teaching video database according to a user's understanding degree within a time range of the granular sub-videos, and combine the selected alternative granular sub-videos into an updated theme sub-video;

[0083] A teaching video database configured to store a plurality of teaching videos and candidate granular sub-videos of the granular sub-videos in each teaching video; and

[0084] The communication unit is configured to send and receive request information for playing teaching videos, configuration information such as the maximum acceptable learning time, expression data, and teaching videos; send and receive instructions for playing updated theme sub-videos and instructions for playing the next theme sub-video.

[0085] In another example, the expression data acquisition unit also includes basic data acquisition through a passive human infrared temperature measurement system, and collects and captures infrared energy emitted by the human body through sensors.

[0086] The teaching management subsystem also includes a physiological health and safety monitoring unit, which is configured to collect basic data through a passive human infrared temperature measurement system, such as a sensor collecting and capturing infrared energy emitted by the human body to generate a stable infrared image, thereby distinguishing whether the user has a fever; it is also configured to automatically modify the camera brightness and camera parameters for the collected eye photos through environmental recognition, use YCbCr technology to perform chromaticity calculations, separate the color and brightness of the image, analyze and calculate to obtain a two-dimensional image distribution result, and establish an eye color analysis model. The analysis and calculation method is: set the normal color and range of the eyes, and the color and range of the eyes in a pathological state; and judge whether there is a physiological disease by color.

[0087] When no physiological disease is detected, step S3 is continued to be performed normally.

[0088] When it is detected that the user has a physiological disease, the user is reminded of the possibility of the disease and is given an option of whether to continue to execute the intelligent teaching program; if so, step S3 is continued, and the first given threshold and the second given threshold within the video playback time range are replaced with the first given threshold and the second given threshold under the disease condition. The first given threshold under the disease condition is greater than the first given threshold under the normal condition; the second given threshold under the disease condition is greater than the second given threshold under the normal condition.

[0089] In addition, the third aspect of the present invention also discloses a teaching device using the above-mentioned intelligent VR teaching system for enhancing video teaching effect, and the device includes a VR helmet and a teaching management platform.

[0090] The intelligent VR teaching system and device for enhancing the video teaching effect provided by the embodiments of the present invention can implement the method embodiments provided above. For the specific function implementation, please refer to the description in the method embodiments, which will not be repeated here. A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0091] The above technical solution is only one implementation mode of the present invention. For those skilled in the art, it is easy to make various types of improvements or modifications based on the principles disclosed in the present invention, and it is not limited to the technical solution described in the above specific embodiments of the present invention. Therefore, the above description is only preferred and does not have a restrictive meaning.

Claims

1. An intelligent VR teaching method for enhancing video teaching effect, comprising the following steps: S1. The user selects a target teaching video and sets the maximum learning time acceptable to the user, and then plays a theme sub-video in the target teaching video, wherein the theme sub-video includes multiple granular sub-videos; S2, collecting the user's facial expression data within the time range of the theme sub-video playback; S3, identifying the expression change characteristics of the user expression data collected in step S2, calculating the user's understanding degree within the self-adaptive playback time range of the theme sub-video, and calculating the user's understanding degree within the granular sub-video time range; S4, judging whether it is necessary to adjust the teaching content of the theme sub-video according to the user's understanding level within the playing time range of the theme sub-video, if necessary, adjust to the next theme sub-video, and then execute step S2; if not, proceed to step S5; S5, judging whether it is necessary to select an alternative granular sub-video from the teaching video database according to the user's understanding degree within the time range of the granular sub-video, and if so, proceeding to step S6; S6. Combining the selected candidate granular sub-videos into an updated theme sub-video, and playing the updated theme sub-video, and then executing step S2; when the remaining learning time is less than the time length of the updated theme sub-video, stop playing the teaching video.

2. The intelligent VR teaching method for enhancing video teaching effect as claimed in claim 1, characterized in that: In step S1, the user selects a target teaching video and sets the maximum acceptable learning time for the user, specifically: using a timed aiming algorithm to select a teaching video to watch and setting the maximum acceptable learning time; The timing anchor point algorithm is that the user wears a VR helmet and configures an aiming point in front of the user's eyes, and the aiming point moves synchronously with the user's head; the user successively stops the aiming point at the position of the teaching video to be selected and the maximum acceptable learning time to be selected, and a click operation is triggered after 2 to 3 seconds, thereby completing the selection operation of the teaching video and the selection operation of the maximum acceptable learning time.

3. The intelligent VR teaching method for enhancing video teaching effect as claimed in claim 1, characterized in that: The step S2 of collecting the user's expression data within the time range of the theme sub-video playback is specifically collecting the user's expression data at a given frequency within the time range of the theme sub-video playback; the expression data includes eye images and mouth images.

4. The intelligent VR teaching method for enhancing video teaching effect as claimed in claim 1, characterized in that: The step S3 specifically includes: Use convolutional neural networks to identify the characteristics of user expression changes; the characteristics of user expression changes include blinking frequency, frowning frequency, and pursing mouth frequency; Calculate the user's course understanding level based on the recognition results of the user's facial expression changes; The calculation formula for the user's course understanding level is: S=a×(A-A0)+b×(B-B0)+c×(C-C0) Among them, a, b and c represent the blink weight, frown weight and pursed lip weight respectively, A0, B0 and C0 represent the preset frequency of blinking, frowning and pursed lip respectively, and A, B and C represent the blink frequency, frowning frequency and pursed lip frequency output by recognition respectively.

5. The intelligent VR teaching method for enhancing video teaching effect as claimed in claim 1, characterized in that: The specific method for determining whether the teaching content of the theme sub-video needs to be adjusted in step S4 includes: If the user's understanding level within the time range of the theme sub-video playback is ≥ the first given threshold, there is no need to adjust the teaching content in the theme sub-video, and the next theme sub-video is played directly, and then step S2 is executed, and when the remaining learning time is less than the time length of the next theme sub-video, the teaching video is stopped; If the user's understanding level within the theme sub-video playback time range is less than the first given threshold, it is necessary to adjust the teaching content of the current theme sub-video according to the user's understanding level within the granular sub-video time range, and then execute step S5.

6. The intelligent VR teaching method for enhancing video teaching effect as claimed in claim 5, characterized in that: The step S5 specifically includes: If the user's understanding level of a granular sub-video in the subject sub-video is less than a second given threshold, randomly selecting an alternative granular sub-video from the alternative granular sub-videos of the granular sub-video; If the user's understanding level of a granular sub-video in the theme sub-video is ≥ a second given threshold, the granular sub-video and its alternative granular sub-videos will no longer be played.

7. The intelligent VR teaching method for enhancing video teaching effect as claimed in claim 6, characterized in that: The step S2 also includes safety monitoring of the user's physiological health, specifically including: Basic data collection is carried out through the passive human infrared temperature measurement system. The sensor collects and captures the infrared energy emitted by the human body to generate a stable infrared image, thereby distinguishing whether the user has a fever. The user's eyes are imaged through a camera, the chromaticity of the eyes in the image is calculated, the color and brightness of the image are separated, the two-dimensional image distribution result is obtained through analysis and calculation, an eye color analysis model is established, and the presence of physiological diseases is determined based on the eye color analysis; When no physiological disease is detected, step S3 is continued normally; When it is detected that the user has a physiological disease, the user is reminded of the possibility of the disease and is given the option of whether to continue the intelligent teaching program; if so, continue to step S3 and replace the first given threshold and the second given threshold within the video playback time range with the first given threshold and the second given threshold in the disease case.

8. The intelligent VR teaching method for enhancing video teaching effect as claimed in claim 7, characterized in that: The first given threshold value under the disease condition is greater than the first given threshold value under the normal condition; the second given threshold value under the disease condition is greater than the second given threshold value under the normal condition.

9. An intelligent VR teaching system using the intelligent VR teaching method for enhancing video teaching effect as claimed in claim 1, characterized in that: The system includes a VR helmet interaction subsystem and a teaching management subsystem; The VR helmet interaction subsystem includes An interactive interface unit, configured to provide a teaching video, an acceptable maximum learning time dialog box, play the teaching video, and set the acceptable maximum learning time; The expression data collection unit is configured to use a camera to collect the user's expression data at a given frequency within the playing time range of the theme sub-video; the expression data includes eye images and mouth images; The expression data acquisition unit is equipped with two cameras, one camera collects the user's eye image, and the other camera collects the user's mouth image; as well as A communication unit, configured to perform data interaction with the teaching management subsystem; The teaching management subsystem includes An expression recognition unit configured to recognize the changing features of the user's expression within the playing time range of the theme sub-video; An understanding degree calculation unit configured to calculate the user's understanding degree within the time range of the theme sub-video playback and to calculate the user's understanding degree within the time range of the granular sub-video; An adjustment judgment unit is configured to judge whether the teaching content of the theme sub-video needs to be adjusted according to the user's understanding level within the playing time range of the theme sub-video; an alternative video generating unit configured to select alternative granular sub-videos from a teaching video database according to a user's understanding degree within a time range of the granular sub-videos, and combine the selected alternative granular sub-videos into an updated theme sub-video; A teaching video database configured to store a number of teaching videos and candidate granular sub-videos of the granular sub-videos in each teaching video; The physiological health and safety monitoring unit is configured to collect basic data through a passive human infrared temperature measurement system, such as a sensor collecting and capturing infrared energy emitted by the human body to generate a stable infrared image, thereby distinguishing whether the user has a fever; it is also configured to perform color calculations on the collected user's eye photos, separate the color and brightness of the image, analyze and calculate to obtain a two-dimensional image distribution result, and establish an eye color analysis model; as well as A communication unit configured to send and receive information requesting to play a teaching video, configuration information such as an acceptable maximum learning time, expression data, and a teaching video; Send and receive instructions to play updated theme sub-videos and instructions to play the next theme sub-video.

10. An intelligent VR teaching device using the intelligent VR teaching system for enhancing video teaching effect as described in any one of claims 8-9.

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